def test_deconvolve_spectral(self):
self.actualSetUp(add_errors=True)
dirty_imagelist = invert_list_arlexecute_workflow(self.vis_list,
self.model_imagelist,
context='2d',
dopsf=False,
normalize=True)
psf_imagelist = invert_list_arlexecute_workflow(self.vis_list,
self.model_imagelist,
context='2d',
dopsf=True,
normalize=True)
dirty_imagelist = arlexecute.persist(dirty_imagelist)
psf_imagelist = arlexecute.persist(psf_imagelist)
deconvolved = deconvolve_list_arlexecute_workflow(
dirty_imagelist,
psf_imagelist,
self.model_imagelist,
niter=1000,
fractional_threshold=0.1,
scales=[0, 3, 10],
threshold=0.1,
gain=0.7)
deconvolved = arlexecute.persist(deconvolved)
deconvolved = arlexecute.compute(deconvolved, sync=True)
export_image_to_fits(
deconvolved[0], '%s/test_imaging_%s_deconvolve_spectral.fits' %
(self.dir, arlexecute.type()))
def _invert_base(self,
context,
extra='',
fluxthreshold=1.0,
positionthreshold=1.0,
check_components=True,
facets=1,
vis_slices=1,
gcfcf=None,
**kwargs):
centre = self.freqwin // 2
dirty = invert_list_arlexecute_workflow(self.vis_list,
self.model_list,
context=context,
dopsf=False,
normalize=True,
facets=facets,
vis_slices=vis_slices,
gcfcf=gcfcf,
**kwargs)
dirty = arlexecute.compute(dirty, sync=True)[centre]
export_image_to_fits(
dirty[0], '%s/test_imaging_invert_%s%s_%s_dirty.fits' %
(self.dir, context, extra, arlexecute.type()))
assert numpy.max(numpy.abs(dirty[0].data)), "Image is empty"
if check_components:
self._checkcomponents(dirty[0], fluxthreshold, positionthreshold)
def _predict_base(self, context='2d', extra='', fluxthreshold=1.0, facets=1, vis_slices=1, **kwargs):
vis_list = zero_list_arlexecute_workflow(self.vis_list)
vis_list = predict_list_arlexecute_workflow(vis_list, self.model_list, context=context,
vis_slices=vis_slices, facets=facets, **kwargs)
vis_list = subtract_list_arlexecute_workflow(self.vis_list, vis_list)[0]
vis_list = arlexecute.compute(vis_list, sync=True)
dirty = invert_list_arlexecute_workflow([vis_list], [self.model_list[0]], context='2d', dopsf=False,
normalize=True)[0]
dirty = arlexecute.compute(dirty, sync=True)
assert numpy.max(numpy.abs(dirty[0].data)), "Residual image is empty"
export_image_to_fits(dirty[0], '%s/test_imaging_predict_%s%s_%s_dirty.fits' %
(self.dir, context, extra, arlexecute.type()))
maxabs = numpy.max(numpy.abs(dirty[0].data))
assert maxabs < fluxthreshold, "Error %.3f greater than fluxthreshold %.3f " % (maxabs, fluxthreshold)
def test_deconvolve_and_restore_cube_mmclean_facets(self):
self.actualSetUp(add_errors=True)
dirty_imagelist = invert_list_arlexecute_workflow(self.vis_list,
self.model_imagelist,
context='2d',
dopsf=False,
normalize=True)
psf_imagelist = invert_list_arlexecute_workflow(self.vis_list,
self.model_imagelist,
context='2d',
dopsf=True,
normalize=True)
dirty_imagelist = arlexecute.persist(dirty_imagelist)
psf_imagelist = arlexecute.persist(psf_imagelist)
dec_imagelist = deconvolve_list_arlexecute_workflow(
dirty_imagelist,
psf_imagelist,
self.model_imagelist,
niter=1000,
fractional_threshold=0.1,
scales=[0, 3, 10],
algorithm='mmclean',
nmoment=3,
nchan=self.freqwin,
threshold=0.01,
gain=0.7,
deconvolve_facets=8,
deconvolve_overlap=8,
deconvolve_taper='tukey')
dec_imagelist = arlexecute.persist(dec_imagelist)
residual_imagelist = residual_list_arlexecute_workflow(
self.vis_list, model_imagelist=dec_imagelist, context='2d')
residual_imagelist = arlexecute.persist(residual_imagelist)
restored_list = restore_list_arlexecute_workflow(
model_imagelist=dec_imagelist,
psf_imagelist=psf_imagelist,
residual_imagelist=residual_imagelist,
empty=self.model_imagelist)
restored = arlexecute.compute(restored_list, sync=True)[0]
export_image_to_fits(
restored, '%s/test_imaging_%s_overlap_mmclean_restored.fits' %
(self.dir, arlexecute.type()))
def test_deconvolve_and_restore_cube_mmclean(self):
self.actualSetUp(add_errors=True)
dirty_imagelist = invert_list_arlexecute_workflow(self.vis_list, self.model_imagelist, context='2d',
dopsf=False, normalize=True)
psf_imagelist = invert_list_arlexecute_workflow(self.vis_list, self.model_imagelist, context='2d',
dopsf=True, normalize=True)
dec_imagelist, _ = deconvolve_list_arlexecute_workflow(dirty_imagelist, psf_imagelist, self.model_imagelist, niter=1000,
fractional_threshold=0.01, scales=[0, 3, 10],
algorithm='mmclean', nmoments=3, nchan=self.freqwin,
threshold=0.1, gain=0.7)
residual_imagelist = residual_list_arlexecute_workflow(self.vis_list, model_imagelist=dec_imagelist,
context='wstack', vis_slices=51)
restored = restore_list_arlexecute_workflow(model_imagelist=dec_imagelist, psf_imagelist=psf_imagelist,
residual_imagelist=residual_imagelist,
empty=self.model_imagelist)[0]
restored = arlexecute.compute(restored, sync=True)
export_image_to_fits(restored, '%s/test_imaging_%s_mmclean_restored.fits' % (self.dir, arlexecute.type()))
